Hypergraphs for Joint Multi-view Reconstruction and Multi-object Tracking

Martin Hofmann, Daniel Wolf, Gerhard Rigoll; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3650-3657

Abstract


We generalize the network flow formulation for multiobject tracking to multi-camera setups. In the past, reconstruction of multi-camera data was done as a separate extension. In this work, we present a combined maximum a posteriori (MAP) formulation, which jointly models multicamera reconstruction as well as global temporal data association. A flow graph is constructed, which tracks objects in 3D world space. The multi-camera reconstruction can be efficiently incorporated as additional constraints on the flow graph without making the graph unnecessarily large. The final graph is efficiently solved using binary linear programming. On the PETS 2009 dataset we achieve results that significantly exceed the current state of the art.

Related Material


[pdf]
[bibtex]
@InProceedings{Hofmann_2013_CVPR,
author = {Hofmann, Martin and Wolf, Daniel and Rigoll, Gerhard},
title = {Hypergraphs for Joint Multi-view Reconstruction and Multi-object Tracking},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}